code: def get_data_creator(backend="torch"):
      if backend == "torch":
  
          def data_creator(config):
              import torch
              from torch.utils.data import TensorDataset, DataLoader
  
              tsdata = get_tsdataset()
              x, y = tsdata.roll(lookback=7, horizon=1).to_numpy()
              return DataLoader(
                  TensorDataset(
                      torch.from_numpy(x).float(),
                      torch.from_numpy(y).float(),
                  ),
                  batch_size=config["batch_size"],
                  shuffle=True,
              )
  
          return data_creator
      if backend == "keras":
  
          def data_creator(config):
              tsdata = get_tsdataset()
              tsdata.roll(lookback=7, horizon=1)
              return tsdata.to_tf_dataset(
                  batch_size=config["batch_size"], shuffle=True
              )
  
          return data_creator
  
imports: from unittest import TestCase
  import pytest
  from bigdl.chronos.utils import LazyImport
  import numpy
  from bigdl.chronos.data import TSDataset
  import pandas
  from ..None import op_torch, op_tf2, op_distributed, op_inference
label_desc: Use image node_id 3 for calling a global function with example usage: get_data_creator(backend) and returns: data_creator, data_creator, DataLoader, tsdata